2022
DOI: 10.3934/dsfe.2022022
|View full text |Cite
|
Sign up to set email alerts
|

High-Frequency Trading with Machine Learning Algorithms and Limit Order Book Data

Abstract: <abstract><p>In this paper, we examine the usefulness of machine learning methods such as support vector machines, random forests and bagging for the extraction of information from the limit order book that can be used for intraday trading. For our empirical analysis, we first get 50 raw features from the LOBSTER message file and order book file of the iShares Core S &amp; P 500 ETF for the time period from 27.06.2007 to 30.04.2019 and then construct 18 higher-level features (aggregated to 5 mi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 16 publications
0
1
0
Order By: Relevance
“…This ensured that the predictions remained relevant and accurate, even as market conditions changed. Mangat et al (2022) went a step further by introducing a price prediction algorithm based on a hybrid model that combined both convolutional neural networks (CNN) and recurrent neural networks (RNN). CNNs were particularly useful in capturing spatial features in the data, such as sudden spikes or drops in prices.…”
Section: High-frequency Trading (Hft)mentioning
confidence: 99%
“…This ensured that the predictions remained relevant and accurate, even as market conditions changed. Mangat et al (2022) went a step further by introducing a price prediction algorithm based on a hybrid model that combined both convolutional neural networks (CNN) and recurrent neural networks (RNN). CNNs were particularly useful in capturing spatial features in the data, such as sudden spikes or drops in prices.…”
Section: High-frequency Trading (Hft)mentioning
confidence: 99%